In a decision tree, variables used closer to the root are generally:

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BlackTom AI · Accepted Answer

Question restatement: In a decision tree, variables used closer to the root are generally:

Option 1: 'Less important features' — This is incorrect. Features near the root typically have a larger impact on the prediction because they split the data earlier and influence more downstream decisions. Calling them less important contradicts how decision trees prioritize splits to maximize information gain or reduce impurity.

Option 2: 'More important features' — This is correct. Variables near the root are the first splits and thus affect a larger portion of the data, contributing more to the overall predictive power of the tree. Their higher position indicates greater importance in the model's structure.

Option 3: 'Equally important as all others' — This is not accurate. While some trees may have features with similar effects, the standard interpretation is that root-proximate features carry more influence because they determine the major branches early in the decision process.

Option 4: 'Only relevant for numeric predictions' — This is false. The relevance of root-proximity applies to both numeric (regression) and categorical (classification) trees. A feature’s position at the root reflects its usefulness in partitioning the target outcome, regardless of the prediction type.

Further context to solidify understanding: In many algorithms (e.g., CART, information gain-based trees), the root node is chosen by the feature that provides the best overall split across the entire dataset. This early split reduces impurity or maximizes information gain, making that feature inherently more influential in shaping the rest of the tree. Thus, root-adjacent variables are considered more important, and later splits refine the predictions but generally have diminishing overall impact.

BA 3551 (002 & 003) Exam 2

In a decision tree, variables used closer to the root are generally:

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